NYC Monday Morning Yellow Taxi Rides

Overview and data sources

This assignment uses the trip records data of New York City Yellow Medallion Taxicabs and looks for spatial patterns in Monday morning taxi rides to work. The data sources used are NYC Taxi and Limousine Commission (TLC) trip record data for trip records, NYC Department of City Planning for neighbourhood boundary information and NYC Open Data for NYC population figures by neighbourhood

Variables

Variables of interest in trip records data set are, * tpep_dropoff_datetime - Drop location information * Dropoff_longitude - Drop location longitude * Dropoff_latitude - Drop location latitude

Cleaning and preparation of data

As observations with no drop location and arrival time information are of no use, they are filtered out. Trip records on Monday mornings between 8 and 10 pm are then exported into a CSV file which will be fed into GeoDA for further processing.

#Set the working directory
setwd("~/Documents/RWorkspace/Spatial Data Science/NYC Trips")

#Download data
yellow <- read.csv("yellow_tripdata_2016-01.csv", header=T)

#Filter out observations which don't have dropoff datetime and co-ordination information
yellow <- with(yellow, yellow[!is.na(tpep_dropoff_datetime) & !is.na(dropoff_longitude) & !is.na(dropoff_latitude),])

#Cast datetime field to POSIXlt
yellow$tpep_dropoff_datetime <- strptime(x = as.character(yellow$tpep_dropoff_datetime),
                                         format = "%Y-%m-%d %H:%M:%S", tz="America/New_York")

#Get monday morning rides between 8 and 9 am. Ignore January 1 information, as it is a holiday.
yellow <- with(yellow, yellow[(tpep_dropoff_datetime)$wday == 1 
                           & (tpep_dropoff_datetime)$mday != 1 
                           & (tpep_dropoff_datetime)$hour %in% c(8, 9),])

names(yellow)[names(yellow)=="tpep_dropoff_datetime"] <- "dropoff_datetime"
names(yellow)[names(yellow)=="tpep_dropoff_datetime"] <- "dropoff_datetime"
names(yellow)[names(yellow)=="Dropoff_longitude"] <- "dropoff_longitude"
names(yellow)[names(yellow)=="Dropoff_latitude"] <- "dropoff_latitude"

#Export the data to be fed to GeoDA
write.csv(yellow, file = "trips.csv")

Point map

Boundary and population information

The observations in trip records data have no information regarding their neighbourhoods, neither the boundaries nor the Ids. CARTO was used to compare the geolocation information obtained from the trip records data with the boundary information in New York City Population By Neighborhood Tabulation Areas dataset. The function of interest here is the geometry function ‘ST_Intersects’. As population information of the neighbourhoods is also added to the aggregated data and exported into a shape file, for further processing with GeoDA.

  SELECT nyc.the_geom, 
       nyc.borocode, 
       nyc.boroname, 
       nyc.ntacode, 
       nyc.ntaname, 
       popbyneigh.population AS "population",
       count(*) as "noRides"
  FROM nynta AS "nyc"
  JOIN trips_edited AS "trp"
  ON 
    ST_Intersects(trp.the_geom, nyc.the_geom)
  JOIN popbyneigh
  ON nyc.ntacode = popbyneigh.nta_code
  WHERE popbyneigh.year = 2010
  GROUP BY
    nyc.the_geom, 
    nyc.ntacode, 
    nyc.ntaname,
    nyc.borocode, 
    nyc.boroname,
    popbyneigh.population
  ORDER BY
      nyc.borocode, 
      nyc.boroname, 
      nyc.ntacode

Choropleth map

The rides per capita variable (density variable) is contructed in GeoDa is contructed and the choropleth maps are generated for the count and density variables.